( 您好!臺灣時間:2022/09/29 00:02
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::


研究生(外文):Po-Wei Cheng
論文名稱(外文):Applying Rotation Gradient and Particle Filter Techniques to Real-Time Human Detection and Tracking
外文關鍵詞:Human TrackingParticle FilterColor HistogramHOG
  • 被引用被引用:0
  • 點閱點閱:130
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0

With the advent of new technology and the innovation, human detection and tracking have become popular research topics. The scope of applications covers the security and surveillance, intelligent transportation systems, and home care systems. However, due to the complexity and changing background, people’s scale size, and occlusion problems, there are still limited practical applications. In order to improve the effect of detection and tracking, this study proposes a histogram oriented gradient method combined with particle filter to achieve real-time tracking of the human body. Detecting methods can be generally divided into three steps, namely, background construction, foreground subtracting and background updating. In reality, the design of those three stages is more complex in the dynamic environment. Therefore, we use histogram oriented gradient and support vector machine for training, building human descriptor in detection, and finding possible human in the films. Our tracking method uses particle filtering technique. We approach from particle sampling and then select color distribution as target feature. We find weights by computing Bhattacharyya coefficient between the target and candidate particles, and use the weighted average to estimate the final target location. We improve particle filter method by adding edge feature to overcome the shortcomings of using only one single color feature and achieve better tracking accuracy. The tracking error is compared by RMSE (root mean squared error). If only the color feature is considered, the tracking error is about 74.18. With the help of edge feature the error is reduced to approximately 61.84. Experimental results verify that the proposed system has higher tracking accuracy and is more robust.

摘 要 i
致 謝 iv
目 錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1研究背景 1
1.2研究目的 2
1.3研究方法 2
1.4論文架構 3
第二章 相關技術及運用探討 4
2.1 人體偵測 4
2.1.1 物體的特徵 4
2.1.2 偵測常用的方法 6
2.1.3 旋轉梯度特徵(Histogram of Oriented Gradient) 8
2.1.4 支持向量機 11
2.2 人體追蹤 16
2.2.1 剪影追蹤(Silhouette tracking) 16
2.2.2 核心追蹤(Kernel tracking) 17
2.2.3 點追蹤(Point tracking) 19
第三章 系統架構與設計 26
3.1 系統架構 26
3.1.1 硬體架構 26
3.1.2 軟體架構 28
3.2 偵測階段 28
3.3 LIBSVM 36
3.4 追蹤階段 37
3.5 開發平台 45
第四章 實驗結果與分析 46
4.1 實驗架構 46
4.2 人體偵測結果 46
4.3 人體追蹤結果 53
4.4 實驗分析結果 66
第五章 結論與未來展望 70
5.1 結論 70
5.2 未來展望 71
參考文獻 72

[1] 影像視訊科技未來展望與願景,http://bit.kuas.edu.tw/~cvgip10/IPPR20/09.pdf。
[2] Fast Texture Synthesis using Tree-structured Vector Quantization, http://graphics.stanford.edu/papers/texture-synthesis-sig00/texture.pdf.
[3] C. Harris an M. Stephens, “A combined corner and edge detector,” in Proc. of The 4th Alvey Vision Conf., Manchester, UK, pp.147-151, 1988.
[4] D. Lowe, “Distinctive image features from scale-invariant key point,” International Journal of Computer Vision, vol. 60, no. 2, pp.91-100, Jan. 2004.
[5] C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” in Proc. of IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, Cambridge, MA, USA, vol. 2, pp.246-252, June 1999.
[6] M. Kass, A. Witkin and D. Terzopoulos, “Snakes: active contour models,” Int. Journal Computer Vision, vol. 1, pp.321-331, Jan. 1998.
[7] J.-Y. Gan, X.-H. Cao and J.-Y. Zeng, “Combining heritance adaBoost and random forests for face detection,” in Proc. of 9th IEEE Int. Conf. on Signal Processing (ICSP), Jiangmen, China, pp.666-669, Oct. 2010.
[8] Q. Tao, G.-W. Wu, F.-Y. Wang and J. Wang, “Posterior probability support vector machines for unbalanced data,” IEEE Trans. on Neural Networks, vol. 16, pp.1561-1573, Nov. 2005.
[9] J. S. Mashford, “A neural network image classification system for automatic inspection,” in Proc. of IEEE Int. Conf. on Neural Networks, Perth, WA, vo1. 2, pp.713-717, Nov./Dec. 1995.
[10] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. of IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, San Diego, CA, USA, vol. 1, pp.886-893, 2005.
[11] Finding People in Images and Video Sequences, http://lear.inrialpes.fr/people/dalal/NavneetDalalThesis.pdf.
[12] M.B. Kaaniche and F. Bremond, “Tracking hog descriptors for gesture recognition,” in Proc. of IEEE Int. Conf. on Advanced Video and Signal Based Surveillance, Genova, Italy, pp.140-145, Sep. 2009.
[13] Z.-X. Chen, J. Wang and Z. Liquan, “Pedestrian detection based on the combination of hog and background subtraction method,” in Proc. of IEEE Int. Conf. on Transportation, Mechanical and Electrical Engineering, Changchun, Jilin, China, pp.527-533, Dec. 2011.
[14] H. Jung, J. K. Tan, S. Ishikawa and T. Morie, “Applying hog feature to the detection and tracking of a human on a bicycle,” in Proc. of IEEE Int. Conf. on Control, Automation and Systems, Gyeonggi-do, Korea, pp.1740-1743, Oct. 2011.
[15] C. Cortes and V. Vapnik, “Support vector networks,” Int. J. Machine Learning, vol. 20, pp.1-25, Feb. 1995.
[16] A. Yilmaz, O. Javed and M. Shah, “Object tracking: a survey,” ACM Computing Surveys, vol. 38, no. 4, pp.1-45, Dec. 2006.
[17] M. Kass, A. Witkin and D. Terzopoulos, “Snake: active contour models,” Int. J. Computer Vision, vol. 1, pp.321-331, Jan. 1988.
[18] D. Comaniciu, V. Ramesh and P. Meer, “ Real-time tracking of non-rigid objects using mean shift,” in Proc. of IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, Hilton Head Island, SC, vol. 2, pp.142-149, Sep. 2000.
[19] G. R. Bradski, “Computer video face tracking for use in a perceptual user interface,” Intel Technology Journal, vol. 2, pp.1-15, Aug. 1998.
[20] G. Welch and G. Bishop, An introduction to the Kalman filter, Department of Computer Science, University of North Carolina at Chapel Hill, pp.1-16, Apr. 2004.
[21] E. B. Meier and F. Ade, “Tracking cars in range images using the condensation algorithm,” in Proc. of Image Science Swiss Federal Institute of Technology (ETH), Tokyo, Japan, pp.129-134, Oct. 1999.
[22] F. Gustafsson, F. Gunnarsson,N. Bergman, U. Forssell, J. Jansson, R. Karlsson and P.-J. Nordlund, “Particle filters for positioning, navigation, and tracking,” IEEE Trans. on Signal Processing, vol. 2, pp.452-437, Feb. 2002.
[23] S. K. Zhou, R. Chellappa and B. Moghaddam, “Visual tracking and recognition using appearance-adaptive models in particle filters,” IEEE Trans. on Image Processing, vol. 13, pp.1491-1506, Nov. 2004.
[24] P. Pan and D. Schonfeld, “Video tracking based on sequential particle filtering on graphs,” IEEE Trans. on Image Processing, vol. 20, pp.1641-1651, June 2011.
[25] 數位攝影機,http://www.cdvideo.com.tw/SONY_HDD/SR220/sr220.html。
[26] 伽瑪校正,http://www.idsvision.com.tw/phpbb2/viewtopic.php?t=480。

第一頁 上一頁 下一頁 最後一頁 top